In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
One example is below.
Q: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
A: No
Rationale: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Q: Manning and Schütze argue that, even though not quite correct, language text can be modeled as stationary, ergodic random processes BIBREF29, an assumption that we follow. Moreover, given the diversity of language production, we assume this stationary ergodic random process with finite alphabet $\mathcal {A}$ denoted $X = \lbrace X_i, -\infty < i < \infty \rbrace $ is non-null in the sense that always $P(x_{-m}^{-1}) > 0$ and

This is sometimes called the smoothing requirement. 
 Question: Is the assumption that natural language is stationary and ergodic valid?
A:
It is not completely valid for natural languages because of diversity of language - this is called smoothing requirement.